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Symbolic Dynamics, Poincaré Plot Analysis and Compression Entropy Estimate Complexity in Biological Time Series

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Book cover Complexity and Nonlinearity in Cardiovascular Signals

Abstract

Methods from nonlinear dynamics have shown new insights into alterations of the cardiovascular system under various physiological and pathological conditions, and thus providing additional prognostic information. In this chapter prominent complexity methods of non-linear dynamics as symbolic dynamics, Poincaré plot analyses, and compression entropy are introduced and their algorithmic implementations and application examples in clinical trials are provided. Especially, we will give their basic theoretical background, their main features and demonstrate their usefulness in different applications in the field of cardiovascular and cardiorespiratory time series analyses.

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  1. Institute of Innovative Health Technologies IGHT, Ernst-Abbe-Hochschule, Jena, Germany

    Steffen Schulz & Andreas Voss

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  1. Steffen Schulz
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  1. Department of Electronics, Informatics and Bioengineering, Politecnico di Milano, Milan, Italy

    Riccardo Barbieri

  2. Department of Information Engineering & Bioengineering and Robotics Research Center “E. Piaggio”, University of Pisa, School of Engineering, Pisa, Italy

    Enzo Pasquale Scilingo

  3. Department of Information Engineering & Bioengineering and Robotics Research Center “E. Piaggio”, University of Pisa, School of Engineering, Pisa, Italy

    Gaetano Valenza

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Schulz, S., Voss, A. (2017). Symbolic Dynamics, Poincaré Plot Analysis and Compression Entropy Estimate Complexity in Biological Time Series. In: Barbieri, R., Scilingo, E., Valenza, G. (eds) Complexity and Nonlinearity in Cardiovascular Signals. Springer, Cham. https://doi.org/10.1007/978-3-319-58709-7_2

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